This library includes various experimental or otherwise special-purpose extras for use with PyMC that have been extracted from the exoplanet project. It's primary components are some helper functions for non-linear optimization and some custom distributions.

You'll need a Python installation, and it can often be best to install PyMC using conda so that it can handle all the details of compiler setup. This step is optional, but I would normally create a clean conda environment for projects that use PyMC:

# Optional
conda create -n name-of-my-project pymc
conda activate name-of-my-project

The easiest way to install this package is using pip:

python -m pip install -U pymc-ext

This will also update the dependencies like PyMC, which is probably what you want because this is only tested on recent versions of that package.

When PyMC added a warning to the pm.find_MAP function, we implemented a custom non-linear optimization framework in exoplanet because it is often useful to be able to optimize (at least) some parameters when initializing the sampler for many problems in astrophysics (and probably elsewhere). While pm.find_MAP no longer complains, the pymc_ext.optimize function is included here for backward compatibility even though it should have similar behavior to pm.find_MAP. To use this function, you'll do something like the following:

import pymc_ext as pmx

with model:
    soln = pmx.optimize(vars=[var1, var2])
    soln = pmx.optimize(start=soln, vars=[var3])

Most of the custom distributions in this library are there to make working with periodic parameters (like angles) easier. All of these reparameterizations could be implemented manually without too much trouble, but it can be useful to have them in a more compact form. Here is a list of the included distributions and short descriptions:

• pmx.unit_disk: Two dimensional parameters constrained to live within the unit disk. This will be useful when you have an angle and a magnitude that must be in the range from zero to one (for example, an eccentricity vector for a bound orbit). This distribution is constrained such that the sum of squares along the zeroth axis will always be less than one. Note that the shape of this distribution must be two in the zeroth axis.
• pmx.angle: An angle constrained to be in the range -pi to pi. The actual sampling is performed in the two-dimensional vector space (sin(theta), cos(theta)) so that the sampler doesn't see a discontinuity at pi. As a technical detail, the performance of this distribution can be affected using the regularization parameter which helps deal with pathological geometries introduced when this parameter is well/poorly constrained. The default value (10.0) was selected as a reasonable default choice, but you might get better performance by adjusting this.

It's important to note that these are not Distribution objects, but rather functions that will add Distribution objects to the model, and return the reparameterized variable of interest. The ergonomics of this interface are questionable, but it's easier to maintain this interface than one that implements custom Distribution objects.

Copyright 2020-2022 Dan Foreman-Mackey and contributors.

pymc-ext is free software made available under the MIT License. For details see the LICENSE file.